Data engineering is the hidden bottleneck in every analytics project.
Most BI projects spend 60–70% of their timeline on data — connecting sources, writing ETL, debugging transformations, handling schema changes. This is before a single dashboard gets built. Organizations end up with brittle pipelines that break when the source system updates, maintained by one engineer who becomes a single point of failure.
Pipelines that are pre-built, tested, and maintained.
IntelliFabric deploys pre-built pipeline templates for the most common enterprise source systems — SAP, Oracle EBS, Salesforce, Dynamics 365, SQL Server, PostgreSQL, Shopify, and dozens more. These templates encode the extraction logic, transformation rules, and error handling for each system. You get pipelines that work on day one, not after months of engineering.
From connection to insight in four steps.
Source system inventory
During week one, your Folio3 team inventories your source systems — ERPs, databases, APIs, flat files. For each source, the appropriate pre-built connector template is selected and configured for your environment.
Pipeline deployment
Connector templates are deployed into your Microsoft Fabric workspace as Fabric Data Factory pipelines. Credentials are provisioned, schemas are mapped, and initial full loads are executed to populate OneLake.
Incremental refresh configuration
Each pipeline is configured for incremental refresh — extracting only records changed since the last run. This minimizes load on source systems and keeps pipeline run times short even as data volumes grow.
Monitoring and alerting
Every pipeline run is logged to a pipeline monitoring dashboard. Failed runs trigger alerts. Schema changes in source systems surface as warnings before they cause data quality issues downstream.